
"""simple docstring"""



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


__lowercase						:     Union[str, Any]							      =		r"""\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"""





@add_start_docstrings(SCREAMING_SNAKE_CASE_					)
class 					lowerCAmelCase			(				SCREAMING_SNAKE_CASE_					):
     """simple docstring"""







     __lowercase						:Union[str, Any]          =    "rag"
     __lowercase						:Tuple          =    True




     def __init__(      self ,		UpperCamelCase__=None ,		UpperCamelCase__=True ,		UpperCamelCase__=None ,		UpperCamelCase__=None ,		UpperCamelCase__=None ,		UpperCamelCase__=None ,		UpperCamelCase__=None ,		UpperCamelCase__=" / " ,		UpperCamelCase__=" // " ,		UpperCamelCase__=5 ,		UpperCamelCase__=300 ,		UpperCamelCase__=768 ,		UpperCamelCase__=8 ,		UpperCamelCase__="wiki_dpr" ,		UpperCamelCase__="train" ,		UpperCamelCase__="compressed" ,		UpperCamelCase__=None ,		UpperCamelCase__=None ,		UpperCamelCase__=False ,		UpperCamelCase__=False ,		UpperCamelCase__=0.0 ,		UpperCamelCase__=True ,		UpperCamelCase__=False ,		UpperCamelCase__=False ,		UpperCamelCase__=False ,		UpperCamelCase__=True ,		UpperCamelCase__=None ,		**UpperCamelCase__ ,		)					->  Dict:







            '''simple docstring'''





            super().__init__(
                bos_token_id=snake_case__ ,		pad_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		decoder_start_token_id=snake_case__ ,		forced_eos_token_id=snake_case__ ,		is_encoder_decoder=snake_case__ ,		prefix=snake_case__ ,		vocab_size=snake_case__ ,		**snake_case__ ,		)
            assert (
                "question_encoder" in kwargs and "generator" in kwargs
            ), "Config has to be initialized with question_encoder and generator config"
            lowerCamelCase_       							=		kwargs.pop('''question_encoder'''							)
            lowerCamelCase_       							=		question_encoder_config.pop('''model_type'''							)
            lowerCamelCase_       							=		kwargs.pop('''generator'''							)
            lowerCamelCase_       							=		decoder_config.pop('''model_type'''							)

            from ..auto.configuration_auto import AutoConfig

            lowerCamelCase_       							=		AutoConfig.for_model(snake_case__ ,		**snake_case__							)
            lowerCamelCase_       							=		AutoConfig.for_model(snake_case__ ,		**snake_case__							)

            lowerCamelCase_       							=		reduce_loss
            lowerCamelCase_       							=		label_smoothing
            lowerCamelCase_       							=		exclude_bos_score
            lowerCamelCase_       							=		do_marginalize

            lowerCamelCase_       							=		title_sep
            lowerCamelCase_       							=		doc_sep
            lowerCamelCase_       							=		n_docs
            lowerCamelCase_       							=		max_combined_length

            lowerCamelCase_       							=		dataset
            lowerCamelCase_       							=		dataset_split
            lowerCamelCase_       							=		index_name

            lowerCamelCase_       							=		retrieval_vector_size
            lowerCamelCase_       							=		retrieval_batch_size
            lowerCamelCase_       							=		passages_path
            lowerCamelCase_       							=		index_path
            lowerCamelCase_       							=		use_dummy_dataset

            lowerCamelCase_       							=		output_retrieved

            lowerCamelCase_       							=		do_deduplication

            lowerCamelCase_       							=		use_cache

            if self.forced_eos_token_id is None:
                   lowerCamelCase_       							=		getattr(self.generator ,		'''forced_eos_token_id''' ,		snake_case__							)




     @classmethod
     def    _lowerCAmelCase		(      cls ,		UpperCamelCase__ ,		UpperCamelCase__ ,		**UpperCamelCase__							)					->  PretrainedConfig:







            '''simple docstring'''





            return cls(question_encoder=question_encoder_config.to_dict() ,		generator=generator_config.to_dict() ,		**snake_case__							)




     def    _lowerCAmelCase		(      self							)					->  Tuple:







            '''simple docstring'''





            lowerCamelCase_       							=		copy.deepcopy(self.__dict__							)
            lowerCamelCase_       							=		self.question_encoder.to_dict()
            lowerCamelCase_       							=		self.generator.to_dict()
            lowerCamelCase_       							=		self.__class__.model_type
            return output
def 	lowerCamelCase					(	UpperCamelCase     :				int = 60_08_51_47_51_43							)   ->    int:

		try:
				_lowerCamelCase				   =  int(UpperCamelCase							)
		except (TypeError, ValueError):
				raise TypeError('Parameter n must be int or castable to int.'							)
		if n <= 0:
				raise ValueError('Parameter n must be greater than or equal to one.'							)
		_lowerCamelCase				   =  1
		_lowerCamelCase				   =  2
		while i * i <= n:
				while n % i == 0:
						_lowerCamelCase				   =  i
						n //= i
				i += 1
		if n > 1:
				_lowerCamelCase				   =  n
		return int(UpperCamelCase							)


if __name__ == "__main__":
				print(F'''{solution() = }''')